Today, a variety of algorithms for text detection and text recognition have been devised and applied to various fields of application. The technologies for detecting or recognizing texts in natural images have gained a lot of attentions in recent years as a key component for reading texts in those natural images, and related patent applications have been filed as well.
With images of training set and training algorithms devised, the technologies trains an apparatus, and then the trained apparatus applies various text recognition algorithms to identify texts.
Given a natural image as an input, a technology for detecting texts may find out a position and a size of each text in the natural image and a technology for recognizing texts may identify a set of characters located at the position. A text in an image could be detected by a device itself like ADAS, i.e., advanced driver-assistance systems, or inputted by a user through a touch interface. Thus, the technology for detecting texts may be implemented more easily than the technology for recognizing texts.
The conventional text recognition methods may be categorized into two types. FIGS. 1A and 1B are respective drawings illustrating each type of the methods.
FIG. 1A is a drawing illustrating a method of segmenting an input image by each of words in the input image and holistically recognizing each of the words in each of corresponding word-level bounding boxes. And, FIG. 1B is a drawing illustrating a method of segmenting an input image by each of characters in the input image, recognizing each of the characters in each of corresponding character-level bounding boxes and combining the recognized characters to determine an appropriate word with a certain meaning.
However, the conventional word-level processing method such as FIG. 1A may be vulnerable to variations in text length, variations in spacing between characters, and languages such as Chinese or Japanese that have no spaces in its text. And the conventional character-level processing method such as FIG. 1B may suffer from ambiguity between similar-shaped characters, e.g., {I,1,1}, {0,O}, {5,S}.
As such, all the conventional text recognition approaches have such drawbacks as mentioned above. Thus, the applicant comes up to the invention of a robust and novel scene text recognition method. Particularly, a novel text recognition method with a high efficiency in identifying characters with similar shape is devised by reflecting a numerical value, which is determined by referring to feature information of at least one or more of neighboring characters adjacent to a specific character as a subject to be identified, in a numerical value of feature of the specific character.